

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      Tuple   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if "resnet-50" in model_name:
						a_						:						List[str] 							= ResNetConfig.from_pretrained("""microsoft/resnet-50""")
			elif "resnet-101" in model_name:
						a_						:						int 							= ResNetConfig.from_pretrained("""microsoft/resnet-101""")
			else:
						raise ValueError("""Model name should include either resnet50 or resnet101""")

			a_						:						Any 							= DetrConfig(use_timm_backbone=a__  ,	backbone_config=a__)

			# set label attributes
			a_						:						Optional[int] 							= """panoptic""" in model_name
			if is_panoptic:
						a_						:						Optional[int] 							= 2_5_0
			else:
						a_						:						Any 							= 9_1
						a_						:						int 							= """huggingface/label-files"""
						a_						:						Optional[Any] 							= """coco-detection-id2label.json"""
						a_						:						Union[str, Any] 							= json.load(open(hf_hub_download(a__  ,	a__  ,	repo_type="""dataset""")  ,	"""r"""))
						a_						:						int 							= {int(a__): v for k, v in idalabel.items()}
						a_						:						int 							= idalabel
						a_						:						str 							= {v: k for k, v in idalabel.items()}

			return config, is_panoptic


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						List[str] 							= []

			# stem
			# fmt: off
			rename_keys.append(("""backbone.0.body.conv1.weight""", """backbone.conv_encoder.model.embedder.embedder.convolution.weight"""))
			rename_keys.append(("""backbone.0.body.bn1.weight""", """backbone.conv_encoder.model.embedder.embedder.normalization.weight"""))
			rename_keys.append(("""backbone.0.body.bn1.bias""", """backbone.conv_encoder.model.embedder.embedder.normalization.bias"""))
			rename_keys.append(("""backbone.0.body.bn1.running_mean""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_mean"""))
			rename_keys.append(("""backbone.0.body.bn1.running_var""", """backbone.conv_encoder.model.embedder.embedder.normalization.running_var"""))
			# stages
			for stage_idx in range(len(config.backbone_config.depths)):
						for layer_idx in range(config.backbone_config.depths[stage_idx]):
									# shortcut
									if layer_idx == 0:
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
												    ))
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
												    ))
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
												    ))
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
												    ))
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
												    ))
									# 3 convs
									for i in range(3):
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
												    ))
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
												    ))
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
												    ))
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
												    ))
												rename_keys.append(
												    (
												        f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
												        f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
												    ))
    # fmt: on

			for i in range(config.encoder_layers):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append(
						    (
						        f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
						        f'''encoder.layers.{i}.self_attn.out_proj.weight''',
						    ))
						rename_keys.append(
						    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias'''))
						rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
						rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
						rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
						rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
						rename_keys.append(
						    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight'''))
						rename_keys.append(
						    (f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
						rename_keys.append(
						    (f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
						rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
						# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
						rename_keys.append(
						    (
						        f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
						        f'''decoder.layers.{i}.self_attn.out_proj.weight''',
						    ))
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias'''))
						rename_keys.append(
						    (
						        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
						        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
						    ))
						rename_keys.append(
						    (
						        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
						        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
						    ))
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
						rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight'''))
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight'''))
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias'''))
						rename_keys.append(
						    (f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
						rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

			# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
			rename_keys.extend(
			    [
			        ("""input_proj.weight""", """input_projection.weight"""),
			        ("""input_proj.bias""", """input_projection.bias"""),
			        ("""query_embed.weight""", """query_position_embeddings.weight"""),
			        ("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
			        ("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
			        ("""class_embed.weight""", """class_labels_classifier.weight"""),
			        ("""class_embed.bias""", """class_labels_classifier.bias"""),
			        ("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
			        ("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
			        ("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
			        ("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
			        ("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
			        ("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
			    ])

			return rename_keys


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						int 							= state_dict.pop(a__)
			a_						:						Optional[Any] 							= val


def 					_UpperCAmelCase				(						a__  ,	a__=False):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= """"""
			if is_panoptic:
						a_						:						int 							= """detr."""

			# first: transformer encoder
			for i in range(6):
						# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
						a_						:						Tuple 							= state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
						a_						:						Union[str, Any] 							= state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''')
						# next, add query, keys and values (in that order) to the state dict
						a_						:						Optional[Any] 							= in_proj_weight[:2_5_6, :]
						a_						:						int 							= in_proj_bias[:2_5_6]
						a_						:						Tuple 							= in_proj_weight[2_5_6:5_1_2, :]
						a_						:						str 							= in_proj_bias[2_5_6:5_1_2]
						a_						:						Tuple 							= in_proj_weight[-2_5_6:, :]
						a_						:						Optional[int] 							= in_proj_bias[-2_5_6:]
			# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
			for i in range(6):
						# read in weights + bias of input projection layer of self-attention
						a_						:						Union[str, Any] 							= state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''')
						a_						:						Optional[int] 							= state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''')
						# next, add query, keys and values (in that order) to the state dict
						a_						:						int 							= in_proj_weight[:2_5_6, :]
						a_						:						Any 							= in_proj_bias[:2_5_6]
						a_						:						Optional[int] 							= in_proj_weight[2_5_6:5_1_2, :]
						a_						:						str 							= in_proj_bias[2_5_6:5_1_2]
						a_						:						int 							= in_proj_weight[-2_5_6:, :]
						a_						:						Optional[Any] 							= in_proj_bias[-2_5_6:]
						# read in weights + bias of input projection layer of cross-attention
						a_						:						List[str] 							= state_dict.pop(
						    f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''')
						a_						:						Any 							= state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''')
						# next, add query, keys and values (in that order) of cross-attention to the state dict
						a_						:						str 							= in_proj_weight_cross_attn[:2_5_6, :]
						a_						:						List[str] 							= in_proj_bias_cross_attn[:2_5_6]
						a_						:						Dict 							= in_proj_weight_cross_attn[2_5_6:5_1_2, :]
						a_						:						List[str] 							= in_proj_bias_cross_attn[2_5_6:5_1_2]
						a_						:						List[str] 							= in_proj_weight_cross_attn[-2_5_6:, :]
						a_						:						Optional[Any] 							= in_proj_bias_cross_attn[-2_5_6:]


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						int 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						int 							= Image.open(requests.get(a__  ,	stream=a__).raw)

			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__=None  ,	a__=False):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= get_detr_config(a__)

			# load original model from torch hub
			a_						:						Optional[int] 							= {
			    """detr-resnet-50""": """detr_resnet50""",
			    """detr-resnet-101""": """detr_resnet101""",
			}
			logger.info(f'''Converting model {model_name}...''')
			a_						:						Tuple 							= torch.hub.load("""facebookresearch/detr"""  ,	model_name_to_original_name[model_name]  ,	pretrained=a__).eval()
			a_						:						Any 							= detr.state_dict()
			# rename keys
			for src, dest in create_rename_keys(a__):
						if is_panoptic:
									a_						:						int 							= """detr.""" + src
						rename_key(a__  ,	a__  ,	a__)
			# query, key and value matrices need special treatment
			read_in_q_k_v(a__  ,	is_panoptic=a__)
			# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
			a_						:						Tuple 							= """detr.model.""" if is_panoptic else """model."""
			for key in state_dict.copy().keys():
						if is_panoptic:
									if (
									    key.startswith("""detr""")
									    and not key.startswith("""class_labels_classifier""")
									    and not key.startswith("""bbox_predictor""")
									):
												a_						:						Optional[Any] 							= state_dict.pop(a__)
												a_						:						Optional[int] 							= val
									elif "class_labels_classifier" in key or "bbox_predictor" in key:
												a_						:						Optional[int] 							= state_dict.pop(a__)
												a_						:						int 							= val
									elif key.startswith("""bbox_attention""") or key.startswith("""mask_head"""):
												continue
									else:
												a_						:						Tuple 							= state_dict.pop(a__)
												a_						:						Optional[int] 							= val
						else:
									if not key.startswith("""class_labels_classifier""") and not key.startswith("""bbox_predictor"""):
												a_						:						Optional[int] 							= state_dict.pop(a__)
												a_						:						List[str] 							= val

    # finally, create HuggingFace model and load state dict
			a_						:						Any 							= DetrForSegmentation(a__) if is_panoptic else DetrForObjectDetection(a__)
			model.load_state_dict(a__)
			model.eval()

			# verify our conversion on an image
			a_						:						int 							= """coco_panoptic""" if is_panoptic else """coco_detection"""
			a_						:						List[str] 							= DetrImageProcessor(format=a__)

			a_						:						Any 							= processor(images=prepare_img()  ,	return_tensors="""pt""")
			a_						:						Tuple 							= encoding["""pixel_values"""]

			a_						:						str 							= detr(a__)
			a_						:						Optional[Any] 							= model(a__)

			assert torch.allclose(outputs.logits  ,	original_outputs["""pred_logits"""]  ,	atol=1e-3)
			assert torch.allclose(outputs.pred_boxes  ,	original_outputs["""pred_boxes"""]  ,	atol=1e-3)
			if is_panoptic:
						assert torch.allclose(outputs.pred_masks  ,	original_outputs["""pred_masks"""]  ,	atol=1e-4)
			print("""Looks ok!""")

			if pytorch_dump_folder_path is not None:
						# Save model and image processor
						logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''')
						Path(a__).mkdir(exist_ok=a__)
						model.save_pretrained(a__)
						processor.save_pretrained(a__)

			if push_to_hub:
						# Upload model and image processor to the hub
						logger.info("""Uploading PyTorch model and image processor to the hub...""")
						model.push_to_hub(f'''nielsr/{model_name}''')
						processor.push_to_hub(f'''nielsr/{model_name}''')


if __name__ == "__main__":
	__snake_case				:      List[Any]   		=			argparse.ArgumentParser()

	parser.add_argument(
	    """--model_name""",
	    default="""detr-resnet-50""",
	    type=str,
	    choices=["""detr-resnet-50""", """detr-resnet-101"""],
	    help="""Name of the DETR model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
	)
	parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
	__snake_case				:      str   		=			parser.parse_args()
	convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import math


def 					_UpperCAmelCase				(						a__ = 1_0_0):

			'''simple docstring'''



			a_						:						List[str] 							= sum(i * i for i in range(1  ,	n + 1))
			a_						:						Optional[Any] 							= int(math.pow(sum(range(1  ,	n + 1))  ,	2))
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")


